Candidate had 8 YoE + master’s, Bay Area role, and the package was:

• Base: $230K
• Equity: $845.5K over 4 years (vesting schedule: 40/30/20/10.)
• Annual bonus: $34.5K
• First-year TC: $602.7K

Would you prefer a front-loaded 40/30/20/10 vesting schedule, or a more predictable 25/25/25/25?

At what point does private-company equity start feeling too risky compared with Big Tech RSUs?

And for senior SWE offers, is $600K first-year TC now becoming “top-tier but realistic,” or is this still a rare outlier?

The candidate had a PhD with around 5 YoE, and the package was:

• Base: $300K
• Signing bonus: $100K
• RSU grant: $3M over 4 years
• Annual bonus: $60K
• First-year total comp: $1.21M

Curious what people think: is this a new normal for top ML talent, or just a rare outlier from a company fighting hard for autonomous driving talent?

In an email from the CEO this morning

>Hi team,

>We are in extraordinary times and at a pivotal inflection point to shape the future for our customers. Intuit is an iconic company in a category of one with strong market leadership and multiple diversified growth engines serving consumers, businesses, and accountants. We are well positioned to power the prosperity of our customers and create a bright future, but to do so, we must evolve as a company.

>We have significant momentum across our 3 Big Bets and to fully capitalize on this extraordinary opportunity, we need to move with far greater velocity, urgency, and discipline. We must:

>Scale our AI-native platform to deliver easy, done-for-you experiences. We have already built the foundation; now, we must accelerate delivering undisputed customer benefits with an unmatched combination of data, AI, and human expertise.
Be the center of money for consumers and businesses. We will ensure our platform is their primary financial engine, creating a unified ecosystem so our customers can access, manage, and grow their money with confidence.
Accelerate our authority and right to win in the mid-market. We must scale our impact with far greater velocity, becoming the definitive partner for mid-market businesses and accounting firms, and delivering the industry-specific platform they need to manage complexity and scale at the speed of their ambition.

>Shaping the company for the future
Over the past several months, we have spent significant time evaluating how we focus the company with greater velocity and discipline to achieve what I outlined above. We believe we can serve more customers and deliver breakthrough products that fuel our customers’ success by reducing complexity and simplifying our structure to become a faster, leaner, and more focused company. 

>This required us to make a set of difficult decisions that impact our people. Today, we are reducing our full-time workforce by approximately 17%. These are valued colleagues and friends who have been vital to shaping the company we are today. Saying goodbye is never easy, and I want to acknowledge the weight this news carries for all of us.

>Here are the changes we’re making today and why we’re making them:

>Reducing layers of management. We have identified areas where too many organizational layers have slowed the flow of information and hampered our ability to move with speed. By streamlining our leadership structure, we are empowering our teams who are closer to the customer to make decisions, ensuring we operate as a more agile and accountable organization.
Focusing roles on high impact work. As we simplify our structure, we are reducing the need for coordination heavy roles that were previously required to manage the complexity. This allows us to focus our collective energy on mission-critical work that directly impacts our customers' prosperity.
Bringing our teams closer together to accelerate impact. To accelerate the pace of innovation, we are co-locating our teams within strategic hubs to drive deeper collaboration and impact. This includes winding down our Reno and Woodland Hills offices and reducing our presence in other locations. 
Reducing overlap across TurboTax and Credit Karma. With the integration of TurboTax and Credit Karma now largely complete, we are eliminating overlapping and redundant roles to operate as a single, unified team and platform. 
Reallocating resources to our primary growth engines. We are optimizing our business and reducing investments in certain areas, including Mailchimp, and streamlining parts of our engineering and product organizations to better align resources with our 3 Big Bets.

>These changes are a necessary evolution to reduce complexity and architect an organization that operates with the velocity required to fuel our growth engines. We are fundamentally re-engineering our operating model to increase accountability, accelerate decision making, and ensure our execution is as bold as our strategy.

>Taking care of our people
I understand this news is difficult and that you will want to know what this means for you. People who are being impacted will receive a calendar invite by 9:00 AM PT today titled "Discussion about leaving Intuit" to hear from a leader in their organization about their transition.

>I also want to be clear: these decisions are a reflection of our changing structure, not the individuals in these roles. We are parting with talented, dedicated colleagues who have made significant contributions to Intuit and the customers we serve. 

>Our commitment to treating every individual with dignity and respect is a fundamental part of who we are, and it has never been more important than it is right now. To help everyone leaving, we are providing generous support, including:

>Financial*: Employees will receive generous financial support as they navigate this change and identify their next chapter. In the US, employees will receive 16 weeks of base pay, plus 2 additional weeks for every year at Intuit. They will also have a paid transition period, including July RSU vesting and bonus eligibility, before they leave the company with a last day of July 31, 2026. Employees outside the US will receive a country-specific package, based on local requirements.*
Health care*: We will provide at least 6 months of health insurance support to employees who are leaving and enrolled in Intuit medical plans. They will also have access to free mental health support during the transition period and for up to 60 days after leaving Intuit.*
Career*: Each impacted employee will have access to career transition and job placement services. These include resume development, interviewing techniques, and recruiting and job search help.**
Immigration*: For those who need immigration support, the extended transition period will allow individuals on visas extra time to find their next role. Intuit will also provide access to external immigration experts for advice and support at no cost.*

>To those leaving Intuit, thank you. I want to express my deep gratitude for everything you have done for us. Your contributions have shaped who we are today, and the impact you’ve made on our products, our teams, and our customers will endure. You’ve been part of building something meaningful here, and that will never change.

>Looking ahead 
To those of you staying: I know this is a difficult day. Please support one another, and please don’t hesitate to reach out to your manager or the People team if you need anything.

>As we look ahead, this is an incredible inflection point for our customers and Intuit. We have navigated many moments of strategic reinvention over our 40-year history, and once again, we are making the deliberate, hard choices required to ignite higher-velocity progress across our Big Bets and play to win in our core business. Our customers have ambitious goals, as do we. We have a once in a lifetime opportunity and a lot of important work ahead of us to power economic growth for those we serve

>What will carry us forward in this moment is what always has: supporting one another, staying deeply connected to our customers, and moving forward with purpose and determination.

>Sasan

Interview Rounds Overview

• Round 1: Senior Engineer Interview
• Round 2: Hiring Manager Interview

Preparation Tips & Advice

The candidate had two interview rounds for a summer intern position, after originally applying for a winter intern role.

In the first round, a senior engineer asked questions based on the candidate’s previous work experience. The discussion focused on how to build a good dashboard, how to identify problems, and how to solve them effectively. This round mainly tested SQL at a medium difficulty level, including left joins and different nested table scenarios.

In the second round, the hiring manager asked additional SQL questions and asked the candidate to write SQL code. The difficulty was around medium-easy, with a focus on window functions. The interviewer also asked about the candidate’s experience handling customer feedback, improving customer satisfaction, the tools they had used, and how they implemented past solutions.

The candidate was also given a LeetCode easy-style coding question, similar to merging lists. Throughout the round, the interviewer kept asking follow-up questions based on the candidate’s previous experience.

• Base: $265K
• First-year total comp: $616.5K
• Equity grant: $1.3M over 4 years
• Bonus: $26.5K

Would you take this kind of offer from a fast-growing AI/legal-tech company over a more stable Big Tech senior/staff role?

Just saw a recent Harvey Senior Backend onsite experience, and it’s a good reminder that “simple” coding rounds can still get brutal once edge cases enter the chat.

The onsite was 2 rounds:

Round 1: Text Processing

The candidate was given a sentence and a list of tags, and needed to return the tags that appeared in the sentence as whole-word matches. Matching had to be case-insensitive, and partial matches didn’t count.

At first, this sounds like a basic hash set/tokenization problem: split the sentence, normalize case, strip punctuation, and check each word in O(1).

But then the interviewer extended it to multi-word phrase tags, which changed the problem pretty quickly. Now you need to think about regex, word boundaries, punctuation handling, phrase matching, and avoiding false positives from substring matches.

The candidate said the tricky part wasn’t the core idea, but the string manipulation edge cases.

Round 2: In-Memory File System

The second round was building an in-memory file system from scratch.

Expected features included:

mkdir / directory creation
ls / content listing
write file
read file
append to file
path parsing

The candidate used a generic node class with:

isFile / isDirectory flag
content string
children map: name -> node

Pretty standard trie/tree-style design, but the hard part was getting path traversal and write/append behavior correct under interview pressure.

Saw this Figma senior-level SWE offer data point from the Bay Area, and the timing makes it way more interesting given what happened to the stock.

Senior-level SWE
Master’s, 8 YoE
Bay Area
Base: $227K
RSU grant: $706K over 4 years
Bonus: ~$34K
First-year TC: ~$437K

On paper, this is a very strong senior offer. But the nuance is that Figma’s stock has been extremely volatile since IPO. Recent market data has FIG trading around the low-$20s, far below its post-IPO highs, even after a recent earnings pop;

That makes this offer kind of a Rorschach test.

Bull case: if the RSUs were granted after the stock reset, this could be a very attractive package. You’re getting a strong base, meaningful annual equity, and potential upside if Figma recovers.

Bear case: if the grant was priced anywhere near the hype cycle, that $706K headline number may not feel like $706K anymore.

Figma also just reported 46% YoY revenue growth and raised full-year guidance, with AI monetization starting to show traction, so this is not exactly a “dead software company” story either.

Would you rather take this kind of Figma offer with beaten-down equity and upside potential, or a more stable big-tech offer with less stock volatility?

Data point sourced from chillinterview[dot]com — useful if you’re comparing offers and trying to understand how equity risk changes the real value of a comp package.

This interview experience is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

Interview Rounds Overview

• Round 1: Coding
• Round 2: Coding
• Round 3: Machine Learning
• Round 4: System Design
• Round 5: Behavioral

Full Details & Solution Approach

I had an onsite interview with LinkedIn for a Machine Learning Engineer role. Here's a breakdown of my experience:

Coding 1: I was given the problem of finding a meeting point for N people standing on a line such that the sum of L1 distances to the meeting point is minimized.

• Follow-up: N houses are located at integer positions. I needed to place K routers (only at house locations) to minimize the L2 distance. Calculating the L2 distance was quite tricky.

Coding 2: I was asked how to estimate a user's rating for a movie given the ratings of n users for m movies. The approach involved using k-NN, and I needed to consider the design of appropriate data structures.

Machine Learning: This round covered standard machine learning fundamentals.

System Design: The task was to design an AI-powered personalized InMail system, focusing on how recruiters can more effectively engage with potential candidates. The round required a complete end-to-end design, including UI considerations.

Behavioral: I was asked project-related questions, but I didn't answer well. I failed to demonstrate the broader impact of my projects. I was told a few days later that they would downgrade the offer to a senior level with a one-year cooling-off period. I asked the recruiter, and the compensation (TC) was around 450k, with equity starting in the second year, which I felt was a bit of a lowball offer.

This offer data point is sourced from chillinterview.com—if you’re negotiating offers, it might be worth checking out.

Saw this LinkedIn Data Scientist offer data point and thought it was pretty interesting for anyone benchmarking DS comp in the Bay Area.

Candidate profile: Master’s, 4 years of experience
Role: Data Scientist, Senior-Level
Location: San Francisco Bay Area
Outcome: Accepted

Comp breakdown:

Base salary: $225,000
Signing bonus: $70,000
RSU grant: $440,000 over 4 years
First-year RSU vest: $110,000
Annual bonus: $22,500
Total first-year comp: $427,500

This interview experience is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

>Just saw a recent Meta E5 SWE interview experience focused on AI/ML infrastructure, and it’s a pretty brutal reminder that “doing well” in Big Tech interviews doesn’t always mean an offer.

https://preview.redd.it/h4bqmykrz31h1.png?width=1374&format=png&auto=webp&s=687775a109382ddf916d549daf8d4bc9deef65a3

This offer data point is sourced from chillinterview.com—if you’re negotiating offers, it might be worth checking out.

Someone shared a senior-level AI Research Scientist offer from Thinking Machines Lab in the Bay Area, and the structure is honestly pretty wild.

PhD with 1 YoE, offer accepted:

• Role: AI Research Scientist, Senior-Level
• Location: San Francisco Bay Area
• Base salary: $400,000
• Equity grant: $11,000,000 equity (with 1 year cliff)

This coding question is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

Design Gym Member Check-In System

This offer data point is sourced from chillinterview.com—if you’re negotiating offers, it might be worth checking out.

Saw an interesting Twilio software engineer offer data point for the Bay Area.

Senior-level SWE, 6 years of experience, bachelor’s degree:

• Base: $208K
• Annual bonus: $26K
• Equity grant: $112K RSUs over 4 years
• Vesting: 25/25/25/25
• First-year total comp: $262K

This coding question is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

Coding question:

A command-line tool supports shortcut phrases. Each shortcut maps a text pattern to a command code. Given an input command string, parse it from left to right. At each position, the parser should use the longest shortcut that matches the current prefix. If no shortcut matches, the current character is kept as plain text.

Each dictionary entry has the format "pattern:code":

• pattern is the shortcut text
• code is the command code returned when the pattern is matched

Parsing rules:

• Always choose the longest matching pattern at the current position
• After a pattern is matched, consume the entire pattern
• If no pattern matches, output the current character
• Matched patterns output their command code

Implement the Solution class:

• List<String> parseCommand(String command, List<String> shortcuts) Returns the parsed sequence of command codes and literal characters.

Constraints:

• 1 ≤ command.length
• 0 ≤ shortcuts.length
• All shortcut patterns are unique

This offer data point is sourced from chillinterview.com—if you’re negotiating offers, it might be worth checking out.

Saw a pretty wild Staff-level ML Engineer offer out of the Bay Area:

• Base: $275K
• Year 1 total: ~$752K
• Signing bonus: $350K (Year 1) + $350K (Year 2)
• RSU: $1M (10/10/40/40)
• YoE: ~10

On paper, this looks insane—especially the $700K+ in signing bonuses alone.

This interview experience is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

A candidate recently completed a Staff-level AI Engineer interview loop at Salesforce (Bay Area), and the process turned out to be far more comprehensive than expected.

Interview Rounds Overview

• Round 1: HR Screening
• Round 2: Hiring Manager Chat
• Round 3: Project Deep Dive
• Round 4: AI/ML Fundamentals
• Round 5: Hands
• Round 6: System Design

Preparation Tips & Advice

My interview process included an HR screening, three virtual interview rounds, and an onsite interview.

Virtual Interviews:

• Hiring Manager: This round was primarily behavioral and focused on team fit. I discussed my experience at Amazon, with the interviewer showing interest in my work related to Multi-Agent Orchestration and LLMs.
• Project Deep Dive: I discussed a complex project I had worked on, emphasizing the architectural challenges and trade-offs involved. I recommend preparing a project architecture diagram to avoid any pauses during the discussion.
• AI/ML Fundamentals: I was asked about ML basics such as F1-Score, the difference between classification and regression, and benchmarking. I also discussed model inference, pipeline optimization, and how to service AI Agents. The remaining questions focused on LLMs, requiring knowledge of Transformers, Context Engineering, RAG, Grounding, and Guardrail.

On-site Interview:

The onsite interview was divided into two main sections:

• Hands-on Mini Project (Coding):

I had to implement a web crawler to scrape a specified site and sort the content as required, then export it into a CSV file. I completed the task in Python in about 10 minutes. I discussed optimization strategies and edge cases with the interviewer, who provided positive feedback. I made sure to verbalize my thought process while coding and adhered to best practices. Sharing insights into how the features are implemented in production was well received.

• System Design (Google Sheets):

The task was to design a web service similar to Google Sheets. The interviewer wanted me to focus on the backend and data models, discussing concurrency control (handling multiple users editing simultaneously), storage layer selection, large-scale data load/save, snapshot backup systems, and database tables.

This data point is from chillinterview.com-which aggregates recent comp data and helpful for offer negotiations.

Came across a pretty wild data point for a Staff-level Machine Learning Engineer offer in the Bay Area:

• Company: Unconventional AI
• Base: $350K
• Sign-on: $50K
• Equity(Option): $4M (25% yearly vest)
• First-year total: ~$1.4M

Let that sink in for a second.

>

Came across a recent Meta Software Engineer online assessment (Bay Area, March 2026)

Format

• Platform: CodeSignal
• Duration: 60 min
• 4 coding questions
• Difficulty: ~5/10 (reported)

Full Details & Solution Approach

I received an online assessment after applying. The assessment was on CodeSignal and consisted of four questions:

1. Given an array and a pivot, calculate the number of elements greater than and less than the pivot. Return '>', '<', or '=' based on the relationship between the counts.
2. Given a non-negative array:
   • Find the leftmost non-zero index i, let array[i] = x.
   • Starting from this number, subtract x from each element.
   • If an element becomes less than x, jump to step C.
   • Otherwise, after subtracting x, continue to the next element.
   • Upon reaching the end of the array, jump to step C.
   • Add x to the final result.
   • Go back to step A.
3. Given memory containing 0s and 1s, where 0 represents free and 1 represents occupied. Memory blocks must start at integer multiples of 8:
   • alloc x: Starting from the leftmost block, find x consecutive 0s and change them to 1s.
   • erase ID: Find the memory block corresponding to the ID and delete the block.
4. Find the pair of mountain peaks with the smallest height difference, considering a view gap where only peaks at least viewGap distance apart are visible.

>This data point is from chillinterview.com-which shares real interview experiences and compensation data to help candidates prepare smarter. It also offers clear, well-written system design articles.

Came across a recent Senior Machine Learning Engineer (PhD, ~5 YoE) offer from Uber in the Bay Area and thought it was worth sharing since there aren’t that many clean data points at this level.

Breakdown:

• Base: $239K
• RSU: $420.8K (4-year, front-loaded: 35/28/22/15)
• First-year equity: ~$147K
• Bonus: $35K
• First-year TC: ~$421K